Behavior-based mobility prediction for fast handoffs in wireless LANs

Abstract:

Wireless Networks have been widely adopted into a major part of today's network infrastructure. They have become a popular technology to not only expand the coverage of wired networks but also to interconnect a large wireless network, i.e., wireless mesh networks. As they allow more ﬂexible communication than traditional wired-networks some challenges are raised, such as maintaining a seamless connectivity when Mobile Stations (MSs) move across the cells and dynamically adjusting resources for the transit MSs. Many solutions have proposed using mobility prediction to allow network devices and applications to prepare and adjust before the actual movement. However, none of the existing work considers mobility related to human factors. Therefore, this thesis proposes a technique called Behavior-based Mobility Prediction (BMP) that captures the dynamic behavior of MSs and the network by considering location, group, time-of-day, and duration factors. The proposed BMP is targeted to provide accurate next Access Point (next-AP) predictions for WLANs to minimize the handoﬀ latency. Moreover, the prediction can also apply to resource allocation in any type of Wireless Networks. Our simulation study shows that BMP virtually eliminates the need to scan for APs during handoffs and results in much better overall handff delay compared to existing methods.